{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "99421a68",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright 2021 NVIDIA Corporation. All Rights Reserved.\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "# =============================================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26eabed1",
   "metadata": {},
   "source": [
    "<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
    "\n",
    "# Movie Poster Feature Extraction with ResNet\n",
    "\n",
    "In this notebook, we will use a pretrained ResNet-50 network to extract image features from the movie poster images. \n",
    "\n",
    "Note: this notebook should be executed from within the `nvidia_resnet50` container, built as follows\n",
    "```\n",
    "git clone https://github.com/NVIDIA/DeepLearningExamples\n",
    "git checkout 5d6d417ff57e8824ef51573e00e5e21307b39697\n",
    "cd DeepLearningExamples/PyTorch/Classification/ConvNets\n",
    "docker build . -t nvidia_resnet50\n",
    "```\n",
    "\n",
    "Start the container, mounting the current directory:\n",
    "\n",
    "```\n",
    "nvidia-docker run --rm --net=host -it -v $PWD:/workspace --ipc=host nvidia_resnet50\n",
    "```\n",
    "\n",
    "Then from within the container:\n",
    "\n",
    "```\n",
    "cd /workspace\n",
    "jupyter-lab --allow-root --ip='0.0.0.0'\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "512ba3c2",
   "metadata": {},
   "source": [
    "## Download a pretrained ResNet-50 from NVIDIA GPU cloud\n",
    "\n",
    "First, we install an extra package and restart the kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04eda8e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install ipywidgets tqdm\n",
    "import IPython\n",
    "\n",
    "IPython.Application.instance().kernel.do_shutdown(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2b085131",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import argparse\n",
    "import numpy as np\n",
    "import json\n",
    "import torch\n",
    "from torch.cuda.amp import autocast\n",
    "import torch.backends.cudnn as cudnn\n",
    "\n",
    "import sys\n",
    "sys.path.append('/workspace/DeepLearningExamples/PyTorch/Classification/ConvNets')\n",
    "from image_classification import models\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "70700020",
   "metadata": {},
   "outputs": [],
   "source": [
    "from image_classification.models import (\n",
    "    resnet50,\n",
    "    resnext101_32x4d,\n",
    "    se_resnext101_32x4d,\n",
    "    efficientnet_b0,\n",
    "    efficientnet_b4,\n",
    "    efficientnet_widese_b0,\n",
    "    efficientnet_widese_b4,\n",
    "    efficientnet_quant_b0,\n",
    "    efficientnet_quant_b4,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "598e9143",
   "metadata": {},
   "outputs": [],
   "source": [
    "def available_models():\n",
    "    models = {\n",
    "        m.name: m\n",
    "        for m in [\n",
    "            resnet50,\n",
    "            resnext101_32x4d,\n",
    "            se_resnext101_32x4d,\n",
    "            efficientnet_b0,\n",
    "            efficientnet_b4,\n",
    "            efficientnet_widese_b0,\n",
    "            efficientnet_widese_b4,\n",
    "            efficientnet_quant_b0,\n",
    "            efficientnet_quant_b4,\n",
    "        ]\n",
    "    }\n",
    "    return models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3a79ea57",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_jpeg_from_file(path, image_size, cuda=True):\n",
    "    img_transforms = transforms.Compose(\n",
    "        [\n",
    "            transforms.Resize(image_size + 32),\n",
    "            transforms.CenterCrop(image_size),\n",
    "            transforms.ToTensor(),\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    img = img_transforms(Image.open(path))\n",
    "    with torch.no_grad():\n",
    "        # mean and std are not multiplied by 255 as they are in training script\n",
    "        # torch dataloader reads data into bytes whereas loading directly\n",
    "        # through PIL creates a tensor with floats in [0,1] range\n",
    "        mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)\n",
    "        std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)\n",
    "\n",
    "        if cuda:\n",
    "            mean = mean.cuda()\n",
    "            std = std.cuda()\n",
    "            img = img.cuda()\n",
    "        img = img.float()\n",
    "\n",
    "        if img.shape[0] == 1: #mono image\n",
    "            #pad channels\n",
    "            img = img.repeat([3, 1, 1])\n",
    "        input = img.unsqueeze(0).sub_(mean).div_(std)\n",
    "\n",
    "    return input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "622f25ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_quant_weight_correctness(checkpoint_path, model):\n",
    "    state_dict = torch.load(checkpoint_path, map_location=torch.device('cpu'))\n",
    "    state_dict = {k[len(\"module.\"):] if k.startswith(\"module.\") else k: v for k, v in state_dict.items()}\n",
    "    quantizers_sd_keys = {f'{n[0]}._amax' for n in model.named_modules() if 'quantizer' in n[0]}\n",
    "    sd_all_keys = quantizers_sd_keys | set(model.state_dict().keys())\n",
    "    assert set(state_dict.keys()) == sd_all_keys, (f'Passed quantized architecture, but following keys are missing in '\n",
    "                                                   f'checkpoint: {list(sd_all_keys - set(state_dict.keys()))}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2b450ee1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://api.ngc.nvidia.com/v2/models/nvidia/resnet50_pyt_amp/versions/20.06.0/files/nvidia_resnet50_200821.pth.tar\" to /root/.cache/torch/hub/checkpoints/nvidia_resnet50_200821.pth.tar\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3c64978c4ce042d1b52d6bfaa3251908",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=102491118.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (4): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (5): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
       "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgnet_classes = np.array(json.load(open(\"/workspace/DeepLearningExamples/PyTorch/Classification/ConvNets/LOC_synset_mapping.json\", \"r\")))\n",
    "\n",
    "model_args = {}\n",
    "model_args[\"pretrained_from_file\"] = './nvidia_resnet50_200821.pth.tar'\n",
    "model = available_models()['resnet50'](model_args)\n",
    "\n",
    "model = model.cuda()\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c63102a0",
   "metadata": {},
   "source": [
    "## Extract features for all movies\n",
    "\n",
    "Next, we will extract feature for all movie posters, using the last layer just before the classification head, containing 2048 float values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f6b25b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61951"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import glob\n",
    "\n",
    "filelist = glob.glob('./poster_small/*.jpg')\n",
    "len(filelist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2bdfbfbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./poster_small/0055323.jpg',\n",
       " './poster_small/0114709.jpg',\n",
       " './poster_small/0274711.jpg',\n",
       " './poster_small/0055320.jpg',\n",
       " './poster_small/0054197.jpg',\n",
       " './poster_small/1791658.jpg',\n",
       " './poster_small/1288589.jpg',\n",
       " './poster_small/0365653.jpg',\n",
       " './poster_small/2324928.jpg',\n",
       " './poster_small/6000478.jpg']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filelist[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "937c6853",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "batchsize = 64\n",
    "num_bathces = len(filelist)//batchsize\n",
    "batches = np.array_split(filelist, num_bathces)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cbc97ab6",
   "metadata": {},
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    },
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    },
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      "cannot identify image file './poster_small/0077294.jpg'\n"
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      "cannot identify image file './poster_small/0066075.jpg'\n"
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      "cannot identify image file './poster_small/0070112.jpg'\n",
      "cannot identify image file './poster_small/2323633.jpg'\n"
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      "cannot identify image file './poster_small/0082817.jpg'\n"
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      "cannot identify image file './poster_small/0069165.jpg'\n",
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      "cannot identify image file './poster_small/0000027.jpg'\n"
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      "cannot identify image file './poster_small/0368576.jpg'\n",
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      "cannot identify image file './poster_small/0366178.jpg'\n"
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     ]
    },
    {
     "name": "stderr",
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    {
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      "cannot identify image file './poster_small/0058374.jpg'\n"
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    {
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    {
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      "cannot identify image file './poster_small/1706680.jpg'\n"
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      "cannot identify image file './poster_small/0050545.jpg'\n"
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    {
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      "cannot identify image file './poster_small/0060968.jpg'\n"
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      "cannot identify image file './poster_small/0060176.jpg'\n"
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      "cannot identify image file './poster_small/0079936.jpg'\n"
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      "cannot identify image file './poster_small/0086484.jpg'\n"
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      "cannot identify image file './poster_small/0053214.jpg'\n"
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      "cannot identify image file './poster_small/0064541.jpg'\n"
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      "cannot identify image file './poster_small/6926486.jpg'\n"
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      "cannot identify image file './poster_small/0069745.jpg'\n"
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      "cannot identify image file './poster_small/1745787.jpg'\n"
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      "cannot identify image file './poster_small/0038675.jpg'\n"
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      "cannot identify image file './poster_small/0424237.jpg'\n"
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      "cannot identify image file './poster_small/3365778.jpg'\n"
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    {
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    {
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    {
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    {
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      "cannot identify image file './poster_small/0419641.jpg'\n"
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    {
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      "cannot identify image file './poster_small/0043153.jpg'\n"
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    {
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    {
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      "cannot identify image file './poster_small/0046921.jpg'\n"
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      "cannot identify image file './poster_small/0491764.jpg'\n",
      "cannot identify image file './poster_small/0090642.jpg'\n"
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      "cannot identify image file './poster_small/0057283.jpg'\n",
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      "cannot identify image file './poster_small/0110546.jpg'\n"
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      "cannot identify image file './poster_small/0065243.jpg'\n"
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    {
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      "cannot identify image file './poster_small/0080928.jpg'\n",
      "cannot identify image file './poster_small/0126004.jpg'\n"
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    {
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      "cannot identify image file './poster_small/0123860.jpg'\n",
      "cannot identify image file './poster_small/0123970.jpg'\n",
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      "cannot identify image file './poster_small/0035301.jpg'\n"
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    {
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      "cannot identify image file './poster_small/1330015.jpg'\n",
      "cannot identify image file './poster_small/0062443.jpg'\n"
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      "cannot identify image file './poster_small/0154467.jpg'\n"
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      "cannot identify image file './poster_small/4427076.jpg'\n",
      "cannot identify image file './poster_small/0173714.jpg'\n"
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      "cannot identify image file './poster_small/0090570.jpg'\n"
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      "cannot identify image file './poster_small/0160801.jpg'\n"
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      "cannot identify image file './poster_small/0049854.jpg'\n"
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      "cannot identify image file './poster_small/0367257.jpg'\n"
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      "cannot identify image file './poster_small/0796335.jpg'\n"
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      "cannot identify image file './poster_small/0290820.jpg'\n"
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      "cannot identify image file './poster_small/0025665.jpg'\n"
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      "cannot identify image file './poster_small/0122194.jpg'\n"
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      "cannot identify image file './poster_small/0369903.jpg'\n"
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      "cannot identify image file './poster_small/0033932.jpg'\n"
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      "cannot identify image file './poster_small/0098088.jpg'\n"
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      "cannot identify image file './poster_small/0303151.jpg'\n"
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 99%|█████████▉| 959/967 [13:14<00:06,  1.27it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cannot identify image file './poster_small/1570970.jpg'\n",
      "cannot identify image file './poster_small/0075364.jpg'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████▉| 966/967 [13:21<00:01,  1.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cannot identify image file './poster_small/0099901.jpg'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 967/967 [13:23<00:00,  1.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unable to extract features for 447 images\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "### strip the last layer\n",
    "feature_extractor = torch.nn.Sequential(*list(model.children())[:-1])\n",
    "\n",
    "feature_dict = {}\n",
    "error = 0\n",
    "for batch in tqdm(batches):\n",
    "    inputs = []\n",
    "    imgs = []\n",
    "    for i, f in enumerate(batch):\n",
    "        try:\n",
    "            img = load_jpeg_from_file(f, 224, cuda=True)\n",
    "            imgs.append(f.split('/')[-1].split('.')[0])\n",
    "            inputs.append(img.squeeze())\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "            error +=1\n",
    "    features = feature_extractor(torch.stack(inputs, dim=0)).cpu().detach().numpy().squeeze()  \n",
    "    for i, f in enumerate(imgs):\n",
    "        feature_dict[f] =features[i,:]\n",
    "\n",
    "print('Unable to extract features for %d images'%error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "88a829c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('movies_poster_features.pkl', 'wb') as f:\n",
    "    pickle.dump({\"feature_dict\": feature_dict}, f, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c39dd37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61504"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(feature_dict)"
   ]
  }
 ],
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